Trends in machine learning for signal processing [In the Spotlight]

Tülay Adali; David J. Miller; Konstantinos I. Diamantaras; Jan Larsen

doi:10.1109/MSP.2011.942319

Trends in machine learning for signal processing [In the Spotlight]

Tülay Adali, David J. Miller, Konstantinos I. Diamantaras, Jan Larsen

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

By putting the accent on learning from the data and the environment, the Machine Learning for SP (MLSP) Technical Committee (TC) provides the essential bridge between the machine learning and SP communities. While the emphasis in MLSP is on learning and data-driven approaches, SP defines the main applications of interest, and thus the constraints and requirements on solutions, which include computational efficiency, online adaptation, and learning with limited supervision/reference data.

Original language	English (US)
Article number	6021869
Pages (from-to)	193-196
Number of pages	4
Journal	IEEE Signal Processing Magazine
Volume	28
Issue number	6
DOIs	https://doi.org/10.1109/MSP.2011.942319
State	Published - Nov 2011

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/MSP.2011.942319

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abstract = "By putting the accent on learning from the data and the environment, the Machine Learning for SP (MLSP) Technical Committee (TC) provides the essential bridge between the machine learning and SP communities. While the emphasis in MLSP is on learning and data-driven approaches, SP defines the main applications of interest, and thus the constraints and requirements on solutions, which include computational efficiency, online adaptation, and learning with limited supervision/reference data.",

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Trends in machine learning for signal processing [In the Spotlight]

Abstract

All Science Journal Classification (ASJC) codes

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